Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

What is modal logic

When I started out as a newly hatched PhD student, back in the day, one of the first articles I read and understood (or at least thought that I understood) was Ray Reiter’s classic article on default logic (Reiter, 1980).This was some years after the famous ‘non-monotonic logic’ issue of Artificial Intelligence in which that article appeared, but default logic was still one of the leading approaches, a tribute to the simplicity and power of the theory. As a result of reading the article, I became fascinated by both default logic and, more generally, non-monotonic logics. However, despite my fascination, these approaches never seemed terribly useful for the kinds of problem that I was supposed to be studying—problems like those in medical decision making—and so I eventually lost interest. In fact non-monotonic logics seemed to me, and to many people at the time I think, not to be terribly useful for anything. They were interesting, and clearly relevant to the long-term goals of Artificial Intelligence as a discipline, but not of any immediate practical importance. This verdict, delivered at the end of the 1980s, continued, I think, to be true for the next few years while researchers working in non-monotonic logics studied problems that to outsiders seemed to be ever more obscure. However, by the end of the 1990s, it was becoming clear, even to folk as short-sighted as I, that non-monotonic logics were getting to the point at which they could be used to solve practical problems. Knowledge in action shows quite how far these techniques have come. The reason that non-monotonic logics were invented was, of course, in order to use logic to reason about the world. Our knowledge of the world is typically incomplete, and so, in order to reason about it, one has to make assumptions about things one does not know. This, in turn, requires mechanisms for both making assumptions and then retracting them if and when they turn out not to be true. Non-monotonic logics are intended to handle this kind of assumption making and retracting, providing a mechanism that has the clean semantics of logic, but which has a non-monotonic set of conclusions. Much of the early work on non-monotonic logics was concerned with theoretical reasoning, that is reasoning about the beliefs of an agent—what the agent believes to be true. Theoretical reasoning is the domain of all those famous examples like ‘Typically birds fly. Tweety is a bird, so does Tweety fly?’, and the fact that so much of non-monotonic reasoning seemed to focus on theoretical reasoning was why I lost interest in it. I became much more concerned with practical reasoning—that is reasoning about what an agent should do—and non-monotonic reasoning seemed to me to have nothing interesting to say about practical reasoning. Of course I was wrong. When one tries to formulate any kind of description of the world as the basis for planning, one immediately runs into applications of non-monotonic logics, for example in keeping track of the state of a changing world. It is this use of non-monotonic logic that is at the heart of Knowledge in action. Building on the McCarthy’s situation calculus, Knowledge in action constructs a theory of action that encompasses a very large part of what an agent requires to reason about the world. As Reiter says in the final chapter,

Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems

Logics of propositional control, such as van der Hoek and Wooldridge's CL-PC [14], were introduced in order to represent and reason about scenarios in which each agent within a system is able to exercise unique control over some set of system variables. Our aim in the present paper is to extend the study of logics of propositional control to settings in which these agents have incomplete information about the society they occupy. We consider two possible sources of incomplete information. First, we consider the possibility that an agent is only able to "read" a subset of the overall system variables, and so in any given system state, will have partial information about the state of the system. Second, we consider the possibility that an agent has incomplete information about which agent controls which variables. For both cases, we introduce a logic combining epistemic modalities with the operators of CL-PC, investigate its axiomatization, and discuss its properties.

/pdf/knowledge-and-control-30l00cpoff.pdf

Knowledge and control

Reasoning about capabilities, strategies and knowledge is important in the analysis of multiagent systems. Alternating-time Temporal Epistemic Logic (ATEL) was designed with this aim. Nevertheless, the original interpretation of the language suffered from some counterintuitive properties. These are due to the fact that the strategies the agent applies in worlds that he cannot distinguish may not be uniform, in the sense that the same action is applied in all indistinguishable worlds. Several refinements of the original ATEL semantics were proposed since then. In this paper we argue that the STIT framework can easily account for uniform strategies. STIT is a logic of agency that has been proposed in the 90ies in the domain of philosophy of action. It is the logic of constructions of the form "agent a sees to it that φ". To support our claim, we first present a straightforward solution in STIT logic augmented by a modal operator of knowledge. Then we offer a simplification, by introducing a modal logic of knowledge-based uniform agency, for one-step strategies, alias choices.

Knowing how to play: uniform choices in logics of agency

We propose two alternatives to Xu’s axiomatization of Chellas’s STIT. The first one simplifies its presentation, and also provides an alternative axiomatization of the deliberative STIT. The second one starts from the idea that the historic necessity operator can be defined as an abbreviation of operators of agency, and can thus be eliminated from the logic of Chellas’s STIT. The second axiomatization also allows us to establish that the problem of deciding the satisfiability of a STIT formula without temporal operators is NP-complete in the single-agent case, and is NEXPTIME-complete in the multiagent case, both for the deliberative and Chellas’s STIT.

/pdf/alternative-axiomatics-and-complexity-of-deliberative-stit-51tiwzz5no.pdf

Alternative axiomatics and complexity of deliberative stit theories

We study a version of Propositional Dynamic Logic (PDL) that we call Dynamic Logic of Propositional Assignments (DL-PA). The atomic programs of DL-PA are assignments of propositional variables to true or to false. We show that DL-PA behaves better than PDL, having e.g. compactness and eliminability of the Kleene star. We establish tight complexity results: both satisfiability and model checking are EXPTIME-complete.

/pdf/dynamic-logic-of-propositional-assignments-a-well-behaved-4larpl4o4c.pdf

Dynamic Logic of Propositional Assignments: A Well-Behaved Variant of PDL

Seeing To It That () logic is a logic of agency, proposed in the 1990s in the domain of philosophy of action. It is the logic of constructions of the form ‘agent a sees to it that φ’. We believe that theory can contribute to the logical analysis of multiagent systems. To support this claim, we show that there is a close relationship with more recent logics for multiagent systems. This work extends Broersen et al. (2006, Electron Notes Theor. Comput. Sci., Vol. 157, pp. 23–35) where we presented a translation from Pauly's Coalition Logic to Chellas' logic. Here we focus on Alur, Henzinger and Kupferman's Alternating-time Temporal Logic , and the logic of the ‘fused’ ⋄s[_scstit : _] operator for strategic ability, as described by Horty. After a brief presentation of ATL and the definition of a discrete-time strategic framework slightly adapted from Horty, we give a translation from ATL to the framework, and prove that it determines correct embedding.

https://dspace.library.uu.nl/bitstream/handle/1874/19971/broersen_06_embedding.pdf?sequence=1

Nicolas Troquard

Papers

Knowledge and control

Knowing how to play: uniform choices in logics of agency

Alternative axiomatics and complexity of deliberative stit theories

Dynamic Logic of Propositional Assignments: A Well-Behaved Variant of PDL

Embedding Alternating-time Temporal Logic in Strategic Logic of Agency